Returns to scale

Preparing…

Import modules and prepare data.

Average of rental properties in a given district.

import matplotlib.pyplot as plt
import pandas as pd

from Pyfrontier.frontier_model import MultipleDEA

sample_df = pd.DataFrame(
    {
        "input": [
            1,
            2,
            4,
            6,
        ],
        "output": [
            0.5,
            2,
            4,
            5,
        ],
    }
)
sample_df

	input	output
0	1	0.5
1	2	2.0
2	4	4.0
3	6	5.0

Fit dea model.

The necessity inputs are inputs and outputs. The result has below belongings.

dea = MultipleDEA("VRS", "in")
dea.fit(
    sample_df[["input"]].to_numpy(),
    sample_df[["output"]].to_numpy(),
)

dea.result

[MultipleResult(score=np.float64(1.0), id=0, dmu=DMU(input=array([1]), output=array([0.5]), id=0), x_weight=[1.0], y_weight=[0.0], bias=1.0), MultipleResult(score=np.float64(1.0), id=1, dmu=DMU(input=array([2]), output=array([2.]), id=1), x_weight=[0.5], y_weight=[0.333333], bias=0.333333), MultipleResult(score=np.float64(1.0), id=2, dmu=DMU(input=array([4]), output=array([4.]), id=2), x_weight=[0.25], y_weight=[0.25], bias=0.0), MultipleResult(score=np.float64(1.0), id=3, dmu=DMU(input=array([6]), output=array([5.]), id=3), x_weight=[0.166667], y_weight=[0.333333], bias=-0.666667)]

plt.figure()
plt.plot(sample_df["input"], sample_df["output"])

[<matplotlib.lines.Line2D object at 0x7f54a2afe710>]

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